Uses reflective strategies
to help overcome computational limitations and deal with
uncertainty.

Such a tool would be capable of simulating highly proficient,
subtle, and creative aspects of human decision making in real-world
domains.

The Reflexive System

Proficient decision makers, in relatively familiar situations,
rapidly settle on situation interpretations and plans in the
face of new observations and changing goals. This kind of
situaion understanding and planning can be largely understood
in terms of causal knowledge structures that we call mental
models.

Shruti is unique in combining speed and scalability,
with representation of crucial relational aspects
of real-world decision making. To accomplish speed and scalability,
Shruti utilizes parallel, connectionist processing. To keep
track of relational reasoning, Shruti uses temporal synchrony
of firing in nodes throughout the network to represent information
about the same object. (Most connectionist models represent
fuzzy similarity relations that blur the way objects interact.
Symbolic architectures easily represent relational facts,
but are usually slow and non-scalable.)

In Shruti, activation goes out from nodes that represent
either sensory or linguistic inputs and/or an internally generated
question to be settled, and returns when circuit is completed
by other nodes that are linked to them. Shruti represents
a situation model and/or decision by the emergence of a stable
activation cycle within the network.

Extensions to Shruti were necessary both to improve its representation
of reflexive reasoning and to make it work in conjunction
with a reflective subsystem. Among the extensions that we
have developed are:

Propagation of utility as well as belief, so that Shruti
settles on actions at the same time as it settles on a situation
interpretation.

Reasoning both backward, to find explanations, and forward,
to generate predictions.

Mechanisms required for shifting attention, such as (a)
priming effects that temporarily store results through a
series of attentional shifts, and (b) long-term storage
of aggregated results at the edge of the currently active
part of the network.

Tuning of link strengths through backpropagation.

Attentional limits on dynamic access to long
term memory (LTM) emerge naturally from the computational
structure of Shruti and the neuro-biological constraints it
respects. These inmply that not all information known by the
agent can be brought to bear on a problem at the same time
by purely reflexive processes.

The Reflective System

The reflective subsystem critiques the conclusions of reflexive
processing and guides its subsequent progress. (See the Recognition
/ Metacognition model) Features of the reflective subsystem
include:

Methods for identifying qualitatively different types
of uncertainty based on activation patterns in the reflexive
system

Methods for identifying beliefs most likely to be responsible
for different types of uncertainty

Strategies for shifting attention to beliefs most likely
to be responsible for uncertainty. The results of these
attentional shifts is the activation of previously dormant
information in long-term memory that is likely to help resolve
the uncertainty.

The metacognitive system learns to combine a set of simple
operations: inhibiting recognitional responding, activation
of new information internally by shifting focal attention,
and clamping truth values (which is itself a form of persistent
attention to a node). These operations are simple and both
psychologically and biologically plausible.

The metacognitive system learns to combine these operations
in response to different patterns of uncertainty by reinforcement
and associative learning processes. Through such learning,
the metacognitive system acquires a rich repertoire of uncertainty
handling strategies. These strategies include both domain-specific
and general elements. The result is a dynamic process of evaluating
and improving mental models
of the situation and plans.

Summary

In sum, there are inherent, and dynamic, limits
on the scope of LTM information that can be brought to bear
in interpreting evidence or answering questions. The existence
of such limits means that inference and planning processes must
be capable of (a) dynamically determining the scope of active
human memory from which they draw at any given time, and (b)
of remaining coherent within those limits. This need for fluid
changes in focus introduces the necessity for an adaptive dynamics
of executive attention. The key interaction between the reflexive
and reflective systems is the adaptive direction of focused
attention within the reflexive memory by means of learned metacognitive
behaviors. Recency effects are used to assemble such intermediate
results into composite assessments. The model suggests that
the development of executive attention functions (metacognitive
strategies) may be necessary for, and integral to, the development
of working memory, or dynamic access to LTM.

The focus of the ONR program was on hybrid combinations
of AI and connectionist techniques. We were funded for
a proposal in which a classically "AI" component,
an inferential memory, was implemented as a reflexive
reasoner in a connectionist network (and coupled to
a distributed connectionist "metacognitive"
controller in the proposal). The term "hybrid"
has stuck -- perhaps it now refers mainly to the hetero-duality
of the recognitional and metacognitive components which
we use to model naturalistic decision making.

In this project, we began with a cognitive model of
recognitional and metacognitive behaviors and translated
this model into a connectionst architecture. This research
was done in close collaboration with Lokendra
Shastri and uses Shruti
to implement the recognitional behaviors.

This
project is concerned with how skilled decision makers
assemble and act on situation estimates developed from
mental models. The primary focus of this work has
been training critical thinking skills. In conjunction
with that research, we have been exploring computational
models of both the recognitional skills by which people
reflexively elaborate explanations and predictions in
coherent stories, and the metacognitive skills which experts
develop to critique and interatively improve those initial
assessments. These computational models may be applied
in several ways, including: